Category / Economics

Today I am republishing a piece originally written for Gavekal Dragonomics clients a few months ago, on the US-China rivalry in artificial intelligence. Usually these pieces stay behind the paywall, but with our partner company Evergreen Gavekal we’re making it available for a general audience.

I wrote this piece as a way of sorting out of my own thinking on how to place recent technological trends in the broader story of China’s economic development. I make no claim to be an expert on artificial intelligence: these are just the thoughts of a China watcher trying to absorb what the technologists are saying.

In the escalating trade dispute between the US and China, technology has increasingly become the key issue, overwhelming more traditional economic topics like tariffs, deficits and currency valuations. Both countries see their economic future as depending on their success in high technology, and each is worried they will lose out to the other. One of the most intense areas of focus is artificial intelligence, where recent rapid breakthroughs have captivated investors and the media—and where China has emerged as the main US rival.

In this piece, I will try to provide some nontechnical answers to the questions of the moment: What is artificial intelligence anyway, and why is it a hot topic? Why does China seem to be doing so well in artificial intelligence? And how should we think about the rivalry between the US and China to develop this technology? In brief, I think China will do well in artificial intelligence, in part because the technology is now in a phase that plays to its strengths. But it does not make sense to think of the US and China being engaged in an artificial-intelligence “race” along the lines of the US-Soviet space race.

Machines don’t think, but can do useful stuff

A common-sense definition of artificial intelligence, what a layman might understand the term to mean on the basis of reading the news and watching television, is probably something along the lines of: machines that can think like human beings. Artificial intelligence in this sense does not exist, and according to most researchers in the field there is no prospect of it coming into existence any time soon. There are enormous philosophical and technical challenges in understanding how human minds work and replicating those processes in software, and most of these challenges have not been solved.

A more precise definition of artificial intelligence, closer to that used in the industry, would be: the development of computer systems that can perform tasks associated with human intelligence, such as understanding speech, playing games, or driving cars. In a way the term is misleading, because what is being mimicked is not human intelligence itself, but the practical results of intelligence being applied in specific contexts. Artificial intelligence in this narrow sense does very much exist, and its progress is now attracting feverish interest from business, venture capitalists, governments and the media.

The turning point seems to have come around 2014-15. Since then, software programs have been able to match or exceed human performance in tasks that previously could not be reliably performed by machines: recognizing faces, transcribing spoken words, playing complex games. One landmark that had particular resonance in China was the 2016 victory of Google’s AlphaGo software over a master South Korean player of the board game Go (known as weiqi in Chinese); AlphaGo subsequently also defeated China’s top-ranked player. While chess programs have been beating human masters for years, Go is much more complex; the number of potential board positions is traditionally estimated at 10172, more than the number of atoms in the universe.

How are these feats possible? Most of what is referred to as “artificial intelligence” in the media is a subset of the field known as machine learning, and in particular a subset of machine learning called deep learning. All software works by following clearly specified instructions, known as algorithms, on how to perform a specific task. In machine learning, the algorithms are not fixed in advance, but evolve over time by building data-driven models.

Usually the type of software used in machine learning is called a neural network, because its structure is loosely inspired by the connections between neurons in a human brain. The network takes an input signal and repeatedly processes it into something more useful; what makes the learning “deep” is that there are a large number of “layers” that process the signal. The use of these techniques means that an algorithm can improve its performance of a task by repeated exposure to data. They are particularly useful where writing algorithms the traditional way—by specifying all possible details and eventualities in advance—is cumbersome or impossible. The concept of machine learning dates to the 1960s, and much of the original work underlying today’s approach of deep learning dates to the 1980s and 1990s.

More power, more data

The rapid improvement in the results of specific machine-learning applications in recent years is thus not a result of fresh theoretical breakthroughs. Rather, it has happened because advances in computing power have allowed machine-learning algorithms to run much faster, and the increased availability of very large amounts of structured data have given them much more to work with. The lesson has been that lots of processing of lots of data is required for the algorithms to be effective in finding patterns. This in itself is a sign that machine learning is not very much like human learning: humans can learn quickly from small numbers of examples, by building internal mental models. Machine learning by contrast is a massive and repetitive number-crunching exercise of building up statistical regularities.

Researchers in the field sometimes describe machine-learning algorithms as being “narrow” and “brittle.” Narrow means that an algorithm trained to solve one problem in one dataset does not develop general competencies that allow it to solve another problem in another dataset; an algorithm has to be trained separately for each problem. The Go-playing algorithm is not also capable of analyzing MRI scans. Brittle means that the algorithm only knows its dataset, and can break down if confronted with real-world situations not well represented in the data it learned from. An often-used example is facial-recognition software that is trained on databases consisting largely of photos of white men, which then fails to accurately recognize faces of black women.

But while it is important to understand that machine learning is not a magic wand, it would also not do to underestimate its potential. Machine learning is essentially a way of building better algorithms. That means it could be applied to almost any process that already uses software—which, in today’s world, is quite a lot—as well as many new processes that could not be effectively automated before. The most obvious example is self-driving cars, which can already operate in restricted contexts and could be in general use within a decade. Machine learning is already being used to spot patterns that previously required trained human expertise, such as recognizing financial fraud or early-stage cancers. Because of this broad applicability, enthusiasts call machine learning a “general purpose technology” that, like electricity a century ago, can boost productivity across every part of the economy.

Throwing resources at the problem

The key point is that machine learning has now moved from a pure research phase into a practical development phase. According to Oren Etzioni of the Allen Institute for Artificial Intelligence, all of the major recent successes in machine learning have followed the same template: apply machine-learning algorithms to a large set of carefully categorized data to solve problems in which there is a clear definition of success and failure.

All parts of this procedure are quite resource-intensive. Huge amounts of computing power are required to run the algorithms. The algorithms need huge quantities of data to find patterns. That data must also be first carefully structured and labeled so that the algorithms can draw the right conclusions—for instance, labeling pictures of objects to train an image-recognition algorithm—a process that is extremely labor-intensive. The repeated training and refining of the algorithms also requires a lot of labor by highly skilled workers, whose numbers are necessarily limited. But the reason for the excitement over artificial intelligence is that there is a now a sense that the main remaining constraints on progress are these limitations of resources—and such limitations will be solved over time.

According to its many boosters, China has all of the necessary resources to make progress in machine learning. It has large and well-funded technology companies, including publicly traded giants like Tencent, Alibaba and Baidu, but also private companies with multi-billion-dollar valuations like ByteDance, which runs a popular news app with personalized recommendations, and SenseTime, which specializes in image and facial recognition.

China also has the world’s largest population of internet and mobile phone users, who are creating huge amounts of data on daily basis through their interactions with software. It has a huge population of relatively low-cost college graduates, for doing the more repetitive work of categorizing data. And it also has more top artificial-intelligence researchers than any country other than the US; indeed, many of the top Chinese in the field were educated in the US and have worked for US companies.

China also has a government that has decided that artificial intelligence is going to be the key technology of the future, and that will not accept being left behind. An ambitious national plan released in July 2017 calls for China to lead the world in artificial intelligence theory, technology and applications by 2030 (for detailed analysis of the plan, see these reports by the Paulson Institute and Oxford University’s Future of Humanity Institute). While it is difficult for government plans to create fundamental research breakthroughs on demand, such plans can be good at mobilizing lots of resources. So to the extent that advances in machine learning are now about mobilizing resources, it is reasonable to think China will indeed be able to make lots of progress.

The prospect of China being something close to a peer of the US in a major new technology is a shocking development for many Americans. Everyone knows that China’s economy has grown rapidly and that it has accomplished a lot. But most of its past successes in technology involve deploying things developed elsewhere, such as mobile phones, wind turbines or high-speed trains, on a large scale. China has a per-capita GDP of roughly US$8-9,000 at market exchange rates, lower than Mexico or Turkey—and no one is talking about their dominance in the technologies of the future. It is tempting to try to resolve this paradox by focusing on China’s state support for artificial intelligence, implying that its advantages are unfair. The rivalry is also not a purely economic one, since there are military uses for machine learning.

The paradox is more apparent than real. China is such a huge, diverse and unequal country that averages are not a good guide to the location of the cutting edge. The reality, as anyone who has visited Beijing, Shanghai, or Shenzhen in recent years can attest, is that the income, skills and education levels of its best people can be comparable to those in the US. That elite of course is not representative of all the hundreds of millions of their compatriots, but neither is the Silicon Valley elite representative of middle-class Americans.

The fact that China now has the capability to contribute to cutting-edge research is also in large part a result of its integration with the US: it is the decades of sending top Chinese students to top US universities that have built up the necessary human capital. Rather than say there is a competition between the artificial intelligence sectors in the US and China, it might be more accurate to say that there is a single, global field of machine-learning research that has a significant presence in both North America (Canada also has some top people) and China.

There is no AI race

More fundamentally, it is wrong to think of China and the US as being in a “race” for supremacy in artificial intelligence. Evoking the “space race” with the Soviet Union in the 1960s is the wrong analogy. The space race was about achieving clear technical landmarks defined in advance: first satellite in orbit, first human in orbit, first human on the moon, etc. Today, it’s not clear what the technical landmarks for an artificial intelligence race might be. There is a vague goal of “general purpose artificial intelligence,” which means the kind of thinking, talking computers that are familiar from decades of portrayal in science fiction. But there is no race to make one, since no one knows how.

Rather, there are multiple related efforts going on to make progress on diverse sets of specific technical challenges and applications. If China is the first to achieve some technical breakthrough, that does not prevent the US from also doing so, nor does it guarantee that a Chinese company will control the market for applying that breakthrough. Recall that machine-learning applications can be “narrow” and “brittle”: software that is excellent at predicting, say, the video-watching habits of Chinese will not necessarily also dominate the American market. What we can say is that there are economies of scale and scope in machine-learning research: teams of experts who have successfully developed one machine-learning application themselves learn things that will make them better at developing other machine-learning applications (see this recent paper by Avi Goldfarb and Daniel Trefler for more).

Artificial intelligence is not a prize to be won, or even a single technology. Machine learning is a technique for solving problems and making software. At the moment, it is far from clear what the most commercially important use of machine learning will be. In a way, it is a solution in search of problems. China is making a big push in this area not because it knows what artificial intelligence will be able to do and wants to get there first, but because it does not know, and wants to make sure it does not lose out on the potential benefits. China’s development plan for artificial intelligence is mostly a laundry list of buzzwords and hoped-for technical breakthroughs.

The fact that machine learning is now in a resource-intensive phase does play to China’s strengths. There is an enormous amount of venture-capital money and government largesse flowing toward anything labeled “artificial intelligence” in China, and Chinese companies have had some notable successes in attracting high-profile figures in the field to join them. But fears that China will somehow monopolize the resources needed to make progress in machine learning are fanciful. After all, most of the key resources are human beings, who have minds of their own. And many of the key tools and concepts for creating machine-learning applications are in the public domain.

Will today’s advantages endure?

It is also not certain that the current resource-intensive phase of machine learning will last forever. As is usually the case when limited resources constrain development, people are trying to find ways to use fewer resources: in this case, refining machine learning so that it does not require so much human effort in categorizing data and fine-tuning algorithms. Some of the current buzzwords in the field are “unsupervised learning,” where the machine-learning algorithm is trained on raw data that is not classified or labeled, and “transfer learning,” where an algorithm that has already been trained on one dataset is repurposed onto another dataset, which requires much less data the second time around. Progress in these areas could lessen the advantages of China’s “big push” approach, though of course Chinese researchers would also benefit from them.

China’s current strength in machine learning is the result of a convergence between its own capabilities and the needs of the technology; since both are evolving, this convergence may not be a permanent one. But China’s government is correct to see the current moment as a great opportunity. China was already becoming one of the global clusters of machine-learning research even before the government decided to throw lots of subsidies at the technology. The self-reinforcing dynamics of clusters mean that today’s successes will make it easier for China to attract more machine-learning experts and companies in the future.

The biggest loser from this trend, however, is not the US, which already has well-established clusters of machine-learning research, but smaller nations who would also like to become home to such clusters. European countries, for instance, seem to be struggling to hold their own. The perception that there is a rivalry or race between the US and China ultimately derives from the fact that the two countries are rivals rather than friends. Artificial intelligence may indeed be the first example of a major cutting-edge technology whose development is led by geopolitical competitors—the US and China—rather than a group of friendly nations. The rising tensions between the US and China pose the question of whether a global artificial-intelligence field structured in this way is sustainable, or will be forced to split into national communities. The loss of those exchanges would slow progress in both countries.

The Australian National University’s annual free China Update book is bigger and more interesting than usual this year, in honor of the 40th anniversary of the start of reform and opening up in 1978. It’s got contributions from lots of prominent China economists that I have only begun to work my way through.

Naturally, I immediately checked out the chapter on private sector development by Nick Lardy. It’s quite a useful update to his work on the economic weight and role of the private and state sectors, and includes a careful, data-driven assessment of the resurgence of state enterprises under Xi Jinping. Here is a section where he identifies the symptoms of the crowding-out of private investment by SOEs:

The most plausible explanation of the waning of private investment is crowding out—an explanation supported by several pieces of evidence.

First, the share of bank loans to nonfinancial corporations that went to private firms fell from 57 per cent in 2013 to only 19 per cent by 2015, while the share that went to SOEs almost doubled over the same period—from 35 per cent to 69 per cent.

Second, financing of private firms through microfinance companies stalled after 2015. Lending by these companies grew rapidly from 2008, when the People’s Bank of China and the China Securities Regulatory Commission first issued formal guidelines on microfinance companies. The volume of such lending levelled off at just less than RMB1 trillion in 2014, but has not grown since.

Third, between 2011 and 2015, SOEs’ profits rose by only RMB30 billion, or 1 percentage point, while the investment of these firms rose by almost RMB2 trillion, or more than 20 per cent. Much of the differential between the growth of investment and the growth of profits must have come from increased borrowing from banks.

Fourth, indirect evidence suggests that SOEs have borrowed increasing amounts of funds to cover their financial losses. In 2005, 50 per cent of all SOEs were lossmaking. By 2016, the share of lossmaking SOEs had declined slightly, to 45 per cent. Thus, roughly half of China’s SOEs for more than a decade have been unable to fully cover their cost of capital. Moreover, the magnitude of losses generated by lossmaking firms increased sevenfold, from RMB243 billion in 2005 to RMB1.95 trillion in 2016. As a share of GDP, these losses doubled, from 1.3 per cent in 2006 to 2.6 per cent in 2016.

The interesting question at the moment is how this crowding-out of the private sector evolves in response to the government’s campaign to rein in financial risks. Surprisingly, one of the big casualties has been investment by state entities: there’s been a sharp slowdown in infrastructure investment as the central government has tightened controls over local government fundraising. Mostly as a result, the non-state share of fixed-asset investment rose to 65% in the first half of 2018 from 63% in 2017.

But it would be unusual for tighter financial conditions to really benefit private-sector firms, which tend to be smaller and riskier borrowers than state firms. And there is indeed a great deal of official concern at the moment over small businesses losing access to credit.

(Also see this previous post for other references on the crowding-out of private-sector investment.)

Chinese central bank governor Zhou Xiaochuan recently gave an interview to Caijing magazine, on the occasion of the first anniversary of the renminbi’s inclusion in the currency basket for the IMF’s Special Drawing Right, or SDR, alongside the dollar, euro, pound and yen. This obscure piece of financial infrastructure improbably dominated the headlines for a while, as China waged a public campaign for inclusion. But most people could not figure out why SDR inclusion meant so much to China, and in the end the world seemed to decide that it was mostly a symbolic victory in China’s quest for global status. We haven’t heard much about the SDR since.

Zhou, though, still seems to think that SDR inclusion was a big deal. And since he has for decades been one of the main figures driving the modernization of China’s financial system, his track record is not that of someone who just pursues empty pieces of symbolism. Zhou is already past the normal retirement age and probably will not be in office this time next year, so SDR inclusion is part of his legacy. In the long interview (Chinese text here), he gives what I think is quite a revealing justification:

The entry of the renminbi into the SDR basket will produce a “ratcheting effect” for China’s opening up. This is like the ratchet on the rope in a volleyball net; when the net is tightened the ratchet latches on to the rope, so once it is set in position it cannot go back, cannot reverse. In English there is an expression, “past the point of no return.” Of course, in economics and society there is no absolute “ratchet,” I don’t mean that it’s absolutely impossible for there to be a reverse, just that it is very difficult.

In China’s reform and opening up process, whether in attracting foreign investment, liberalizing foreign trade, reforming the exchange rate, entering the WTO, etc, there were often some small reversals in the middle, or kind of a stop-and-go. But once we took that step, it was very difficult to go back.

After the renminbi entered the SDR, both international institutions and financial markets are using the renminbi more and more; international investors are using the renminbi to invest in the domestic financial market; laws and regulations have been revised; traders and exporters are all using new procedures. If you want to go backward, it is difficult, and the costs are high.

Perhaps another way of putting this is that SDR inclusion is a commitment device. In addition to the practical concerns raised by Zhou, there would also be reputational costs to reversing exchange-rate and capital-account reforms. Since SDR inclusion is contingent on the IMF’s determination that the renminbi is “freely usable,” it could conceivably be reversed if the currency were to stop being freely usable. What future Chinese central bank governor will want to see headlines screaming “IMF expels renminbi from SDR”?

Of course, China over the past year has in fact been de-facto tightening capital controls by stepping up scrutiny of overseas M&A and slowing down approval of foreign-exchange transactions. But it has done so largely by using its regulatory discretion rather than changing formal rules. So perhaps the commitment device is working some.

It is telling though that this justification for SDR inclusion is about consolidating and defending past reforms, rather than advancing new ones, though Zhou clearly wants to see those as well.

Over the past week or so, an impassioned debate has broken out over what should be done to help China’s struggling rust belt in the Northeast. Justin Yifu Lin, perhaps China’s most famous living economist, sparked the debate when his think tank released a long (400+ pages!) report proposing an industrial policy strategy for Jilin, one of the three Northeastern provinces. The report’s recommendations were seemingly innocuous–develop more light industry, tourism, and agriculture-related businesses–but they nonetheless attracted vociferous online criticism.

Why? The summaries in the English-language press (see the SCMP and Caixin) give the impression that it’s a debate over whether government policies should promote light industry, or something else. If that were the case, this would be a typical academic tempest in a teacup. In fact, a lot more is at stake: the debate over what to do about the Northeast (aka Dongbei, aka Manchuria) involves fundamental differences over how to understand Chinese economic history and the development trajectory of countries and regions really develop. The debate over how to help such struggling regions is also one where conventional Western economic wisdom has little to offer, so the field is wide open. After doing some reading on both sides, here’s my guide to the debate (warning: this is a long post).

Is there a connection between nationalism in politics and inward-looking, statist economic policies? The examples of China and Russia (and perhaps Turkey) in recent years suggest that there could be.

But where does this linkage come from? I recently stumbled across a 1987 article by the sociologist Reinhard Bendix, called “The Intellectual’s Dilemma in the Modern World,” in which he articulates this connection rather well. Here is the relevant passage:

There is a family resemblance between the Third World of today and the poor countries of earlier eras. In the sixteenth and seventeenth centuries, English intellectuals and other people reacted to the economic advance of Holland and the Spanish world empire. In the eighteenth century, German writers reacted positively or negatively to the economic and political advance of England and France. In response to the French revolution, German rulers proposed to do for “their” people–by a revolution from above–what the French people had done at great cost by and for themselves. Russian intellectuals during the nineteenth century took standards derived from Western European developments to form counterimages of czarist realities; and in the twentieth century Russian revolutionaries adopted programs and tactics derived from the French revolution and Marxist theory in their overthrow of the czarist regime. …

Every idea taken from elsewhere can be both an asset to the development of a country and a reminder of its comparative backwardness–that is, both a model to be emulated and a threat to its national identity. What appears desirable from the standpoint of progress often appears dangerous to national independence. The revolution in communications since the fifteenth century has been accompanied by ever new confrontations with this cruel dilemma, and the rise of nationalism has been the response nearly everywhere. …

The division is deep over which path the country should follow. Perception of advances abroad are reminders of backwardness or dangers and weaknesses at home. Intellectuals attempt to cope with the ensuing dilemma: whether to adopt the advanced model and invite its attending corruptions, or fall back upon native traditions and risk their inappropriateness to the world of power and progress. This dilemma engenders heated debates and ever-uneasy compromises. People want their country recognized and respected in the world, and to this end they cultivate or revive native traditions. … But the desire to be recognized and respected in the world also calls for the development of a modern economy and government, and this effort at development focuses attention upon ideas and models derived from the advanced society of one’s choice.

I owe the reference to Elena Chebankova’s article “Ideas, Ideology & Intellectuals in Search of Russia’s Political Future” in the spring 2017 issue of Daedalus. She applies the Bendix dichotomy to the Russian situation as follows:

This cruel dilemma forces a split within the intellectual scene of second-wave industrialization states, of which Russia is part. Intellectuals of those countries inevitably face an uneasy choice between losing intellectual and cultural independence by admitting their backwardness and adopting the externally borrowed progressive paradigm, or reaffirming nativism and tradition by holding on to the previously chosen path.

The drama for Russian intellectuals is in the quandary of either adopting the ideology of individual freedom and bourgeois liberties, combined with embracing Western ontology, or clinging to the idiosyncratic centralized modes of governance that could conduct modernization and development, albeit in a risky alternative fashion.

The point is simple: economic policies that are perceived as pursuing convergence with “the West” can be difficult to reconcile with nationalist aspirations to have a country walk its own road. And to the extent that good economic policies actually mean “converging with the West,” nationalism can mean fewer good economic policies.

Of course this relationship is not a necessary one: there is no country that does not have some nationalism in its politics, and good economic policies do not actually have to mean (or be perceived as) “converging with the West.” Deng Xiaoping for one found no difficulty in reconciling his strong Chinese nationalism with liberalizing domestic markets and opening up to global trade. It also seems like Modi in India is managing to pursue a similar combination of nationalist politics with economic restructuring.

But countries with a socialist legacy perhaps face the dilemma more keenly — to a large extent the distinctive “Chinese way” or “Russian way” is, thanks to their history, socialism and the planned economy. And therefore appeals to nationalism can shade more easily into statist economic policies.

In any case, I found this old Bendix article surprisingly useful for thinking about these current questions. It is rather difficult to find online, so I’ve put a copy up on this site; you can download the PDF here.

Our estimates indicate that Northern Song China was richer than Domesday Britain circa 1090, but Britain had caught up by 1400. Also, China as a whole was certainly poorer than Italy by 1300, but at this stage, it is quite possible that the richest parts of China were still on a par with the richest parts of Europe.

By the seventeenth century, however, China as a whole was already substantially behind the leading European economies in the North Sea area, despite still being the richest Asian economy. Even allowing for regional variation within China, it is clear that the Great Divergence between China and Western Europe was already well under way by the first half of the eighteenth century, before the start of the Industrial Revolution.

Although this clearly contradicts the early statements of California School writers such as Pomeranz (2000) and Wong (1997), it is broadly consistent with the later views of Pomeranz (2011), who accepts that his early claim of China on a par with Europe as late as 1800 was exaggerated, and is now willing to settle for an earlier date between 1700 and 1750.

We think this is encouraging, because it shows how engagement between researchers using primarily quantitative methods and those who tend to put more weight on qualitative methods can result in a new consensus that challenges the original position of both sides in a major debate.

The California School were right to claim that, taking account of regional variation, historical differences in economic performance between China and Europe were much less than was once thought. However, the early claims of the California School went a bit too far: China and Europe were already on different trajectories before the Industrial Revolution, as European economic historians have traditionally maintained. The Great Divergence did not begin as late as the nineteenth century.

But you don’t have to take their word for it; Kenneth Pomeranz himself has weighed in with a blogpost reviewing some of this recent research:

A recent paper by Stephen Broadberry, Hanhui Guan and David Daokui Li suggests that Britain must have overtaken the Yangzi Delta in per capita GDP by the first quarter of the 18th century. This is, of course, materially different from my claim in The Great Divergence that the Yangzi Delta had not fallen significantly behind until well into the second half of the 18thcentury, and maybe not until 1800…

I think it is noteworthy that a debate between an early and a late 18th century divergence represents a considerably different intellectual landscape than the one we would have if we relied on Maddison’s GDP numbers, or on the non-quantitative work of David Landes, Deepak Lal, and various others – or for that matter, on an earlier attempt by Guan and Li to estimate comparative GDPs, which had previously claimed that a huge gap already existed in the 15th century. …

Admittedly, that is far from the rough parity I had originally suggested at 1800, and would now be inclined to put at somewhere around 1750 instead; there are some plausible adjustments that I think would narrow the gap further, but that is not really the point for now. Instead I would emphasize that despite continuing disagreements and continuing data problems – the latter of which will probably never be fully solved – we have made some progress in narrowing the range of plausible answers about when and how much divergence occurred in these terms.

On the whole I see this as an example of the virtues of quantification in social science: when disagreements are about empirically measurable quantities, rather than abstract principles, it should be easier to resolve them. But still, how often does that actually happen in economics?

The latest issue of the Journal of Economic Perspectives has a good group of articles on issues in the Chinese economy; there’s a lot to talk about in there, but the piece on education by Hongbin Li, Prashant Loyalka, Scott Rozelle, and Binzhen Wu is particularly worth flagging. It touches on one of the hotter social debates in China over the past few years: whether the massive expansion of college education since 1999 has created an over-supply of graduates, or is just the beginning of the necessary transformation of the education system to meet the needs of a modern economy.

This debate is interesting not only because it is a very consequential one, but also because the two sides tends to use very different styles of argument. The case for the prosecution tends to rely more on close observation of current social phenomena in China (what you might call anecdotal evidence), while the case for the defense tends to rely more on economic theory. A good example of the argument for an education glut is a recent piece by Edoardo Campanella:

Education is never a bad thing in itself, but the move toward “mass universities” of the type that emerged in the West after World War II is occurring too fast. …

China, with a graduate unemployment rate of 16%, is producing more highly educated workers than the economy can absorb. The wage premium for workers with a bachelor’s degree has decreased by roughly 20% in recent years, and new graduates often must accept jobs – such as street cleaning – for which they are vastly overqualified.

As more Chinese students attend university, fewer are graduating from vocational schools, which teach the skills that the economy actually needs. In fact, the demand for qualified blue-collar employees is so high that in 2015 the country’s 23 million textile workers earned, on average, $645 per month – equal to the average college graduate.

Li et al. in the JEP piece note the same widely-reported factoids: that new graduates take a long time to find jobs, and their starting salaries are often of similar levels to manual laborers. But they counter with a combination of theoretical reasons not to be too concerned by these phenomena, and a more involved estimation of the financial returns to education:

In contrast to this common perception of too many college students, we believe that college expansion is a great policy achievement of China. If we assume that the demand for human capital is fixed in the short-run, then given the unprecedented increase in the supply of college graduates since 1999, it is not surprising that the return to college for young college graduates would decline for a time. However, in the long run, human capital investment can lead to investment in physical capital and skill-biased technological changes, which ultimately will increase the productivity of and return to human capital. In addition, regions and cities in developed nations that experience arguably exogenous shocks to the supply of human capital ultimately also experience increases in the productivity of skilled labor due to human capital spillovers. There is no obvious reason to expect that China’s case would be different in this respect.

Moreover, college expansion could well be a result of rising demand for human capital. Our analysis of data from China shows that the return to college education for the labor force as a whole has continued to rise despite the fast expansion of China’s colleges. In particular, the return for those with 5–20 years of work experience has risen from around 34 percent in 2000 to 41 percent in 2009. A possible reason is the rising demand for skilled workers driven by the influx of foreign direct investment and expansion of trade starting from the early 1990s. The high return to college education for experienced workers implies a high lifetime return (the 10-year lifespan return to college education for the year 2000 graduate cohort is as high as 42 percent), which explains why urban students flood into colleges in spite of the seemingly low short-term return.

My own impression is that the education-glut argument is more popular within China, perhaps because it can be more easily illustrated by tales of struggling new graduates. But the statistics that are usually used to support it seem questionable: if a recent college graduate is making the same wage in their first year of work as a migrant worker is making in their 20th, it’s not obvious that actually indicates the market is devaluing a university education. The proper measure is really the lifetime returns to education, and there seems little reason to doubt that today’s college graduates in China are going to have much higher lifetime incomes than today’s migrant workers without a degree. Perhaps the issue is that new graduates do not feel that the gap between themselves and manual workers is as wide as they expected it to be.

Li and his co-authors do point to some worrying evidence that the quality of higher education in China has in fact suffered as the number of students has massively expanded, an issue that Campanella also highlights. But while Campanella recommends making higher education much more restrictive and shunting most students into vocational education, Li and co. argue for decentralizing and deregulating higher education, so that universities are not mainly trying to meet government-set enrollment quotas but are instead competing to deliver a good educational experience.

A more serious problem than any over-supply of college graduates is likely to be the rather shocking under-provision of high school education for rural students, which the JEP article shows is weighing down the overall education level of China’s workforce.